Respiratory disease, the third leading cause of deaths globally, is
considered a high-priority ailment requiring significant research on
identification and treatment. Stethoscope-recorded lung sounds and artificial
intelligence-powered devices have been used to identify lung disorders and aid
specialists in making accurate diagnoses. In this study, audio-spectrogram
vision transformer (AS-ViT), a new approach for identifying abnormal
respiration sounds, was developed. The sounds of the lungs are converted into
visual representations called spectrograms using a technique called short-time
Fourier transform (STFT). These images are then analyzed using a model called
vision transformer to identify different types of respiratory sounds. The
classification was carried out using the ICBHI 2017 database, which includes
various types of lung sounds with different frequencies, noise levels, and
backgrounds. The proposed AS-ViT method was evaluated using three metrics and
achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20
split ratio in terms of unweighted average recall and overall scores
respectively for respiratory sound detection, surpassing previous
state-of-the-art results.

开发了一种名为音频 - 谱图视觉变换器的新方法，将肺部声音转化为称为谱图的可视化表示，通过使用视觉变换器模型对这些图像进行分析以识别不同类型的呼吸声，使用含有各种频率、噪声水平和背景的肺部声音的 ICBHI 2017 数据库对提出的音频 - 谱图视觉变换器方法进行了评估，并在呼吸道声音检测方面取得了 79.1% 和 59.8%（60:40 分割比）以及 86.4% 和 69.3%（80:20 分割比）的未加权平均召回率和总体分数，超过以往最先进的结果。